Classification of SLC family-related genes involved in ferroptosis predicts lung cancer prognosis and immunotherapy response

Lung adenocarcinoma, the most frequent type of lung cancer, is the leading cause of cancer-related deaths worldwide. Ferroptosis, controlled cell death that involves a high degree of iron-dependent lipid peroxidation, has been linked to tumor therapy sensitivity, patient prognosis, and cancer development. The solute carrier superfamily has over 400 members and comprises the largest class of transporters in the human genome. Solute carrier proteins can facilitate the movement of different substrates across biological membranes, which is crucial for physiological activities, including ferroptosis. Here, we developed a new model to further explore the role of the solute carrier family in ferroptosis in the lung adenocarcinoma immunological milieu. We used consensus clustering to classify patients with lung cancer into two subgroups (cluster1 and cluster2). Patients in the cluster1 subtype had a better prognosis and higher immune cell infiltration ratios than those in the cluster2 subtype. Furthermore, to evaluate the prognosis, the immune cell infiltration ratio, and the medication sensitivity of patients with lung adenocarcinoma, we developed gene scores related to the solute carrier family. In conclusion, we successfully developed a model incorporating the solute carrier family and ferroptosis to predict survival and the impact of immunotherapy on patients with lung cancer.

With an extremely high incidence rate worldwide, lung cancer has now overtaken all other cancers as the greatest threat to human health 1 .Based on histological categorization, lung cancer can be divided into small cell lung cancer and non-small cell lung cancer.Non-small cell lung cancers include lung adenocarcinoma (LUAD), lung squamous cell carcinoma, lung adenosquamous cell carcinoma, and large cell lung cancer 2 .LUAD is the predominant histological type, and its prevalence continues to increase 3 .Regarding therapies, targeted immunotherapy has led to notable advances in the treatment of lung cancer in recent years 4 .However, the overall survival rates of patients with LUAD are poor, and these treatments are only effective in some patients 5 .Therefore, we must identify novel and accurate disease markers to effectively treat patients with LUAD.In addition, it is crucial to develop a more accurate prognostic model.
Ferroptosis is an iron-mediated method of cell death.Apoptosis, autophagy, necrosis, and scorch are morphologically related to this mechanism of cell death 6 .Recent studies have shown that ferroptosis is closely related to the pathophysiological mechanisms of various diseases, including cancer, diseases of the nervous system, kidney damage, ischemia-reperfusion injury, and blood disorders 7 .Inducing ferroptosis in cancer cells has emerged as a novel cancer treatment strategy in recent years, particularly for types of cancer that respond poorly to conventional treatments, including radiation, chemotherapy, and immunotherapy 8 .In addition, ferroptosis and tumor immunotherapy are strongly correlated 9 .Ferroptosis-related medications are gradually being used in clinics 10 ; therefore, determining the function of ferroptosis regulators in tumor therapy is becoming increasingly crucial.
The solute carrier (SLC) family, second only to G-protein-coupled receivers, which rank first in number 11 , are essential components of the cell and organelle membranes 12 .In addition, proteins from the SLC family play a critical role in the physiology and transport of a wide range of molecules, including waste removal, nutrient absorption, ion transport, and medication absorption and disposal 13 .Furthermore, SLC family proteins are crucial for ferroptosis and influence the tumor microenvironment 14,15 .However, the role of SLC genes in the

Identification of Differentially Expressed Genes (DEGs) and Signaling Pathways in Patients with Subtypes C1 and C2
Using the R software t-test function, 1,003 DEGs were detected between patients with the subtypes cluster1 and cluster2 (Fig. 3A,B).We then performed functional enrichment analyses on these DEGs using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases.The DEGs were associated with the immunological microenvironment, signaling, material movement, cell development, and death (Fig. 3C,D).Therefore, the two SFRG subtypes had a unique relationship with the immunological microenvironment.Using Gene Set Enrichment Analysis (GSEA), we showed that several immune route-related pathways, including the B-cell pathway, the T-cell pathway, and leukocyte transendothelial migration, were differentially enriched in the two SFRG subtypes (Fig. 3E).

Somatic mutation landscape of the C1 and C2 subtypes
When comparing patients with C1 and C2 subtypes, the most common mutations in the C1 subtype are FAT3 (22.0%) and NAV3 (22.0%), while the C2 subtype is KEAP1 (28.5%) and STK11 (25.1%),In existing research reports, compared with non mutated LUAD patients, LUAD patients with FAT3 mutations have significantly longer immunotherapy progression free survival (PFS) 16 .In addition, among non-small cell lung cancer patients, KEAP1 and STK11 mutated patients are not sensitive to immunotherapy and have shorter disease-free and overall survival 17,18 (Fig. 4).

Mutational landscape of patients with subtypes C1 and C2 in the tumor microenvironment
To better understand the impact of SFRGs typing on immune cell infiltration in LUAD, we examined the differences in the tumor microenvironment between the C1 and C2 subtypes.Overall, the C1 subtype had a higher StromalScore, ESTIMATEScore, and ImmunueScore, and lower TumorPurity than the C2 subtype (Fig. 5A-D).The immune cell infiltration ratio was then examined using MCPcounter and TIMER.Patients with subtype C1 had showed higher percentages of immune cell infiltration than those with subtype C2 (Fig. 5E,F).Furthermore, individuals with subtype C1 had higher rates of immune checkpoints and human leukocyte antigen (HLA) cell infiltration than patients with subtype C2 (Fig. 5G,H).

SFRG analysis using univariate regression analysis
We performed a univariate regression analysis to identify three independent prognostic indicators that could be used in lung adenocarcinoma patients (Fig. 6A).Patients with high SLC11A2 expression have greater overall survival (OS), DSS, and progress free interval (PFI) scores than patients with low SLC11A2 expression.In contrast, patients with high SLC3A2 expression have lower OS, DSS, and PFI scores than patients with low SLC3A2 scores, whereas DSS is unaffected (Fig. 6B-J). www.nature.com/scientificreports/

Construction and validation of gene risk models related to SFRGs
We combined the data on gene expression, survival time, and survival status and used the Lasso-Cox method for regression analysis; consequently, three genes were obtained for the prediction model (Fig. 7A,B).The association between the risk of these three genes and survival status was also investigated.The survival rate of patients remarkably declined as the number of risk variables increased.As anticipated, SLC16A1, SLC3A2, and SLC11A2 were risk factors (Fig. 7C).In the TCGA cohort, patient prognoses were negatively correlated with lung cancer risk scores (Fig. 7D,E).We then verified this finding in a GEO cohort study (Fig. 7F,G).

Correlation of the risk characteristics of SFRGs with subtypes c1 and c2 and the tumor microenvironment
Patients with subtype C2 had higher risk scores than those with subtype C1 (Fig. 8A).We also examined the association between the tumor microenvironment and the SFRG risk score.The risk score of SFRGs was adversely associated with B-cell and CD4 T-cell infiltration (Fig. 8B-D).These findings were validated in the GEO cohort.
To assess the independent predictive value of SFRG risk factors, a multivariate Cox analysis was used to determine the risk variables of SFRGs as independent prognostic factors for patients with LUAD (Fig. 8E,F).

Multivariate nomograms to predict survival
We combined the survival time, survival status, and data for the five characteristics using the R "RMS" software package and used the Cox method to create a nomogram to forecast the survival status of patients with LUAD at 1, 3, and 5 years (Fig. 9A).The calibration curve for the patient nomogram is shown in Fig. 9B.Finally, the prognostic difference between the two groups was examined using the survfit function of the R "survival" package, which was also supported by the Gene Expression Omnibus (GEO) cohort research.The analysis of the receiver operating characteristic (ROC) curve verified that our prediction model was effective (Fig. 9C,D).We used the pRRophytic method to predict drug sensitivity in high-risk and low-risk cancer patients.The sensitivity to rapamycin and SB52334 was higher in high-risk patients than in low-risk patients, whereas sensitivity to BEZ235, cisplatin, RO-3306, and talazoparib was higher in low-risk patients than in high-risk patients (Fig. 9E).

Q-PCR
The gene expression of SLC3A2, SLC16A1, SLC39A14, SLC39A7, SLC1A5, and SLC11A2 in the lung adenocarcinoma cell line A-549 and H1299 is higher than that in the normal lung epithelial cell line Beas-2B (Fig. 10A-L).

Western blot
The protein expression of SLC3A2, SLC11A2, SLC1A5, SLC16A1, SLC39A7, SLC39A14 in the lung adenocarcinoma cell line A-549 and H1299 is higher than that in the normal lung epithelial cell line Beas-2B (Fig. 11A-L).

Discussion
Lung cancer exhibits a complicated pathogenesis and is characterized by a high degree of cell heterogeneity; it frequently invades the surrounding tissue and metastasizes.Therefore, lung cancer is often associated with resistance to a variety of targeted treatments 19 .Ferroptosis is a distinct form of iron-dependent programmed cell death that is characterized by the buildup of lipid peroxides and intracellular reactive oxygen species 20 .The use of the ferroptosis induction strategy to treat cancer is expanding 8 .Furthermore, the prerequisites for using www.nature.com/scientificreports/ferroptosis-targeted medicines in clinical therapy are becoming increasingly established 21 .The SLC family proteins play crucial roles in fundamental biological processes and human disorders 22,23 , especially as transporters of metal ions, amino acids, and lipids in ferroptosis [24][25][26] .Therefore, the SFRGs involved in ferroptosis may be positive indicators of survival outcomes in patients with lung cancer.Based on the characteristics of genes associated with the SLC family in ferroptosis, we developed a model that can be used to determine the prognosis and immunotherapy response of patients with LUAD.Consensus clustering was used to categorize the SFRGs into two subgroups based on their expression.The C1 subtype outperformed the C2 subtype in terms of clinical survival rate, immunological rating, and immune cell infiltration.Furthermore, we chose three SFRGs (SLC16A1, SLC3A2, and SLC11A2) using the Lasso-Cox technique for regression analysis and developed a reliable risk model to divide patients with LUAD into high-and low-risk groups.The overall survival of patients can be accurately predicted using this technique and used as a standalone prognostic indicator for patients with LUAD.The tumor microenvironment includes the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and the extracellular matrix 27 .The onset and progression of ferroptosis alters the immunological milieu, which is crucial for immunotherapy 28 .In addition, ferroptosis produces lipids, and their interactions can influence the release of HMGB1, which controls tumor immunity 29,30 .Importantly, our approach may be helpful in analyzing variations in the immune microenvironment among various patients with adenocarcinoma."Hot" tumors, also known as immune cell infiltrating tumors, are distinguished by a high infiltration of T lymphocytes surrounding and within the tumor 31 .Because T cell infiltration in C1 subtype patients is higher than in C2 subtype patients in our model, C1 subtype patients are more likely to have thermal tumors.
Our study has several significant limitations.First, it did not include samples from hospitalized patients, which could have improved the accuracy of this prediction model.Second, the fundamental experiments were insufficient to fully investigate our model.These issues will be addressed in further studies to ensure that the findings of our work are clinically translatable for future therapeutic applications.

Conclusion
In this study, we developed a prediction model to forecast the outcomes of patients with LUAD and their immune features that may correlate with immune response by thoroughly analyzing SFRGs.Our study examined the significance of SFRGs from a variety of angles and established a benchmark for the future care of patients with LUAD.

Consensus clustering
ConsensusClusterPlus was used to perform the cluster analysis, which involved resampling 80% of the samples 10 times and employing agglomerative partitioning around medoids (PAM) clustering with a 1-Pearson correlation distance.

Identification of DEGs
The significance of each gene in the comparison and control groups was evaluated using the t-test function in R software.The screening conditions for screening were as follows: P < 0.05, FDR < 0.05, and |fold change| >1.5.

Functional enrichment analysis
To compare the different signaling pathways and biological effects between the cluster1 and cluster2 cohorts, GO and KEGG [32][33][34] studies were performed.To acquire the gene set enrichment findings of the gene set, we performed an enrichment analysis on the annotated genes using the R software package "clusterProfiler" (version 3.14.3).

GSEA
To assess the relevant molecular processes and pathways, we divided the samples into two groups based on SFRG expression.Subsequently, we obtained a data subset from the Molecular Signatures Database (http:// www.gsea-msigdb.org/ gsea/ downl oads.jsp).The minimum and maximum number of gene sets was 5 and 5,000, respectively, and the resampling value was 1,000.GSEA software was used for the analysis (version 3.0, http:// softw are.broad insti tute.org/ gsea/ index.jsp).

Somatic mutation analysis
The gene mutations in 451 individuals with lung cancer were examined using the "maftools" function in R software.The results of the analysis are presented as waterfall diagrams.

Immune landscape characteristics among patients in cluster1 and cluster2 subgroups
IOBR is an R software package used to study immunological tumor biology 35 .Based on our expression profile data, the score of infiltrating immune cells in the samples was calculated with IOBR, using ESTIMATE 36 , MCPcounter and TIMER 37,38 .

Figure 1 .
Figure 1.Consensus clustering identifies lung adenocarcinoma subtypes associated with SFRGs.(A) Ten SFRGs were identified.(B) Protein-protein interactions between SFRGs.(C) The gene expression profiles of 10 SFRGs in normal tissues and lung adenocarcinoma samples from the TCGA cohort are depicted in a heatmap.(D) Sample clustering consistency, area under the distribution curve.(E) The cumulative distribution curve from k = 2 to k = 10 in consensus clustering are all columnar.(F) Consensus clustering heatmap at k = 2. (G) Heatmap of SFRG expression in the two different subtypes.(H) Kaplan-Meier curves of OS in cluster1 and cluster2 subtypes.SFRGs, SLC family-related genes; OS, overall survival; TCGA, The Cancer Genome Atlas.

Figure 2 .
Figure 2. Form for clinical information.

Figure 3 .
Figure 3. Differential gene expression and potential signaling pathways in different patient subtypes.(A) Volcano plot quantifying the differentially expressed genes between subtypes C1 and C2.(B) Heatmap showing the expression relationship of the top 40 differential genes between the C1 and C2 subtypes.(C and D) Enrichment analysis of the GO and KEGG pathways.(E) GSEA of the potential immune signaling pathways between patients with the C1 and C2 subtypes.

Figure 4 .Figure 5 .
Figure 4. Comparison of somatic mutations and SFRGs between different subtypes.Top 15 frequently mutated genes in patients with (A) subtypes C1 and (B) subtypes C2.

Figure 8 .
Figure 8. Relationship between the risk characteristics of SFRGs and the tumor microenvironment.(A) Relationship between risk scores among patients with different subtypes.(B-D) Scatter plot showing the correlation between risk score and immune cells (B cells and CD4 T cells).(E) and (F) Multivariate Cox regression analysis that evaluates the independent prognostic value of SFRG risk factors in patients with lung adenocarcinoma.SFRGs, SLC family-related genes.